Generating simulated images that enhance socio-demographic diversity

A system comprising:

• a data-sampling engine configured to:
• receive a plurality of images, wherein each image of the plurality of images depict a respective subject;
• and
• generate a set of training images from the plurality of images, wherein the set of training images include one or more duplicate copies of a training image being associated with a set of target socio-demographic attributes;
• a data-augmentation engine configured to:
• receive the set of training images from the data-sampling engine;
• and
• generate, based on the set of training images, a set of augmented training images, wherein the set of augmented training images include one or more augmented copies of each training image of the set of training images;
• and
• a training engine configured to:
• receive the set of augmented training images from the data-augmentation engine;
• process each augmented training image of the set of augmented training images to train a machine-learning model to generate a simulated image, wherein the simulated image depicts a subject that has one or more visual characteristics defined by the set of target socio-demographic attributes, wherein training the machine-learning model comprises:
• generating an embedding of each augmented training image;
• and
• using the embedding to train a generator component of the machine-learning model to generate a candidate simulated image depicting a candidate subject represented by one or more candidate visual characteristics;
• and
• output the trained machine-learning model.

A computer program product tangibly embodied in a non-transitory machine-readable storage medium including instructions configured to cause one or more data processors to perform actions including:

• receiving a request to generate a simulated image of a subject, wherein the request includes a set of target socio-demographic attributes that define one or more visual characteristics of the subject;
• a step for generating the simulated image of the subject, wherein the simulated image is generated by:
• (i) applying a generator component of a machine-learning model to the set of target socio-demographic attributes to process an embedding representing the set of target socio-demographic attributes to generate a candidate simulated image depicting a candidate subject represented by one or more candidate visual characteristics;
• and (ii) applying a discriminator component of the machine-learning model to the candidate simulated image to generate a set of predicted socio-demographic attributes of the candidate simulated image, determine that the set of predicted socio-demographic attributes substantially match the set of target socio-demographic attributes, and classify the candidate simulated image as the simulated image;
• and
• outputting the simulated image.